Problems such as image-to-image translation, which entails changing the domain of an image while preserving its content, have been extensively studied. Major deep learning innovations have come from this area [1]}, [2]}, producing results of excellent quality. However, unsupervised models have been limited by the rigidity of their domain representations. Many methods can only be trained to map between two domains [2]}, [4]}, or a fixed set of domains [5]}, [6]}, [7]}. Even models designed to accommodate new domains at test-time either rely on restricting the domain to stylistic features [8]}, or requiring re-training for every new example [9]}. Moreover, to the best of our knowledge, no model has the capacity to process sets of examples specifying both the source and target domain at test-time, but rely either on explicit conditioning or on one single example. All these constraints limit the model's ability to understand unseen domains and transfer knowledge between them.
